In causal inference, estimating heterogeneous treatment effects (HTE) is critical for identifying how different subgroups respond to interventions, with broad applications in fields such as precision medicine and personalized advertising. Although HTE estimation methods aim to improve accuracy, how to provide explicit subgroup descriptions remains unclear, hindering data interpretation and strategic intervention management. In this paper, we propose CURLS, a novel rule learning method leveraging HTE, which can effectively describe subgroups with significant treatment effects. Specifically, we frame causal rule learning as a discrete optimization problem, finely balancing treatment effect with variance and considering the rule interpretability. We design an iterative procedure based on the minorize-maximization algorithm and solve a submodular lower bound as an approximation for the original. Quantitative experiments and qualitative case studies verify that compared with state-of-the-art methods, CURLS can find subgroups where the estimated and true effects are 16.1% and 13.8% higher and the variance is 12.0% smaller, while maintaining similar or better estimation accuracy and rule interpretability. Code is available at https://osf.io/zwp2k/.

翻译：在因果推断中，估计异质性处理效应对于识别不同子群对干预措施的响应至关重要，在精准医学和个性化广告等领域具有广泛应用。尽管HTE估计方法旨在提高准确性，但如何提供明确的子群描述仍不清楚，这阻碍了数据解释和策略性干预管理。本文提出CURLS，一种利用HTE的新型规则学习方法，能够有效描述具有显著处理效应的子群。具体而言，我们将因果规则学习构建为离散优化问题，精细平衡处理效应与方差，并考虑规则的可解释性。我们设计了基于最小化-最大化算法的迭代过程，并求解次模下界作为原问题的近似。定量实验和定性案例研究证实，与最先进方法相比，CURLS发现的子群其估计效应和真实效应分别高出16.1%和13.8%，方差降低12.0%，同时保持相当或更优的估计精度和规则可解释性。代码发布于https://osf.io/zwp2k/。